where \(\mu (f_c)\)  and \(\sigma (f_c)\)  denote the mean and variance of extracted feature \(f_c\) . Furthermore, style can be re-assign by \(f_o \cdot \gamma + \beta \) , where \(\gamma \)  and \(\beta \)  are learned parameters. Afterward, [1]} further explored adapting \(f_o\)  to arbitrarily given style by using style features of another extracted feature instead of learned parameters.
Inspired by them, we design a feature fusion method as shown in Fig. REF , and applied it to the UDA task. First, the input images are divided into content images and style images, and their features extracted by a trained classifier are denoted as \(f_c\)  and \(f_s\) , respectively. Secondly, the style features \(\mu (f_c)\)  and \(\sigma (f_c)\)  of content images are cleaned to obtain the object features \(f_o\) . Thirdly, the styles of style images are re-assigned to \(f_o\)  to obtain fused features \(f_{c \rightarrow s}\) . Based on these, the content and style of \(f_{c \rightarrow s}\)  are consistent with the content images and style images respectively. The feature fusion method aims to generate another style of features for the class in the content images, thereby enriching the distribution of that class in the feature space.
